Currently merchandising, that is to say “all studies and application techniques, used separately or conjointly by distributors and producers, with a view to increasing the profitability of the point of sale and the flow of products, by the continuous adaptation of the assortment to the needs of the market and by the suitable presentation of merchandise” (Institut Français du Merchandising, see the document “A. Wellhoff and J. E. Masson, Le merchandising: bases, nouvelles techniques, category management. Dunod, 2005”), requires daily monitoring and a great deal of effort. The presentation and enhancement of the product at the point of sale is an important element that triggers the act of purchase. Thus mass consumption brands sign agreements with distributors in order to ensure the availability of the right product, in the right place, in the right quantity, at the right price and at the right time (the 5Rs of Keppner). This obliges manufacturers to regularly check the correct positioning of their products in shop shelvings.
Checking the placement of each product is at the present time carried out manually. Employees are sent on site in order to inspect each shop and check agreement between the planogram and the actual placement of the products in the shop. Given the large number of shops and the number of products per shop, manual checking is extremely expensive and does not represent an optimum solution.
It is therefore appropriate to propose a method for the automatic construction of a planogram from one or more photographs of shelving.
Though the prior art is rich with regard to image recognition, the way in which we use these techniques for the automatic construction of planograms is novel. The general description of a technique for the automatic extraction of a planogram has already been made in the document “Agata Opalach, Andrew Fano, Fredrik Linaker and Robert Groenevelt, Planogram Extraction Based on Image Processing, WO2009/027839.A2, 2009”, showing to what extent this type of invention may arouse interest. However, our invention is innovative in specifically adapting the image recognition methods to the extraction of planograms.
The image recognition part is itself constructed on the basis of various works of the prior art, including in particular the document “David G. Lowe, Method and Apparatus for Identifying Scale Invariant Features and Use of Same for Locating an Object in an Image, 2004”. The detection of the products could use such algorithms directly. However, in practice, the application thereof for detecting products in images of shelving proves insufficient, in particular when the products have variations in texture, or when different products have very similar visuals, and these algorithms return incomplete and partially erroneous results.